Physiological and protein profiling analysis provides insight into the underlying molecular mechanism of potato tuber development regulated by jasmonic acid in vitro

Background Jasmonates (JAs) are one of important phytohormones regulating potato tuber development. It is a complex process and the underlying molecular mechanism regulating tuber development by JAs is still limited. This study attempted to illuminate it through the potential proteomic dynamics information about tuber development in vitro regulated by exogenous JA. Results A combined analysis of physiological and iTRAQ (isobaric tags for relative and absolute quantification)-based proteomic approach was performed in tuber development in vitro under exogenous JA treatments (0, 0.5, 5 and 50 μΜ). Physiological results indicated that low JA concentration (especially 5 μM) promoted tuber development, whereas higher JA concentration (50 μM) showed inhibition effect. A total of 257 differentially expressed proteins (DEPs) were identified by iTRAQ, which provided a comprehensive overview on the functional protein profile changes of tuber development regulated by JA. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that low JA concentration (especially 5 μM) exhibited the promotion effects on tuber development in various cellular processes. Some cell wall polysaccharide synthesis and cytoskeleton formation-related proteins were up-regulated by JA to promote tuber cell expansion. Some primary carbon metabolism-related enzymes were up-regulated by JA to provide sufficient metabolism intermediates and energy for tuber development. And, a large number of protein biosynthesis, degradation and assembly-related were up-regulated by JA to promote tuber protein biosynthesis and maintain strict protein quality control during tuber development. Conclusions This study is the first to integrate physiological and proteomic data to provide useful information about the JA-signaling response mechanism of potato tuber development in vitro. The results revealed that the levels of a number of proteins involved in various cellular processes were regulated by JA during tuber development. The proposed hypothetical model would explain the interaction of these DEPs that associated with tuber development in vitro regulated by JA. Supplementary Information The online version contains supplementary material available at 10.1186/s12870-022-03852-x.

of tubers, and (b) the biochemical changes resulting in resource storage [1]. At the morphological aspect, potato tubers are derived from underground stems or stolons that undergo a series of development processes, including stolon formation and growth, tuberization induction, tuber initiation and growth [2]. The biochemical change process is accompanied by starch and storage protein accumulation in developing tubers, and coordinated by the expression of genes involved in the biosynthesis of these substances [2,3]. Tuber formation and development is controlled by endogenous and external factors, which ensure that the time of tuber formation coincides with favorable developmental and environmental conditions [4]. Under favorable conditions, particularly shortday photoperiods and cool temperatures, some mobile signals such as FLOWERING LOCUS T (FT)-like protein (StSP6A) and StBEL5 mRNA originate in the leaf and then move to the stolon tip to regulate the onset of tuber formation [5][6][7]. Among these systems, the signaling and crosstalk of phytohormones play critical roles in tuberization [6,8,9].
Jasmonates (JAs) are lipid-derived phytohormones that regulate plant development and defense processes [10]. In potato, JAs have high tuber-inducing activity and promote tuber formation [11][12][13][14]. During the early stages of tuber formation, tuber initiation is associated with an increase of endogenous JA level [15]. JA stimulates tuber initiation and enlargement by antagonizing GA (Gibberellin), which exerts opposite actions in the cortical microtubule orientation to GA [8,15]. The roles of JA in tuber formation mainly contribute to promoting radial cell expansion, meristem enlargement and early vascular tissue differentiation, thus facilitating the movement of substances to stolon tips [16,17]. JA also stimulates the biosynthetic activity of plastid apparatus in the apical meristem cells of tubers for starch accumulation [18]. Further evidences for the involvement of JAs in potato tuberization have derived from affecting the expression of enzymes or proteins involved in JAs biosynthesis or signaling pathway. Lipoxygenases (LOXs) produce hydroperoxyl fatty acids that are precursors of JA and related compounds, which control potato tuber morphogenesis [19]. Suppression mutants produced by expressing antisense POTLX-1 gene exhibit a significant reduction in the LOX activity of stolons and tubers, and a disruption of tuber formation [19]. Over-expression of JA carboxyl methyltransferase gene JMT in transgenic potato plants can enhance tuber yield and size as well as in vitro tuberization frequency [20]. A homologue of cytoplasmic/nuclear chitooligosaccharide-binding lectin might participate in JA-mediated signaling pathway involved in potato tuberization [21]. Overexpression of StJAZ1-like (a suppressor of JA signaling) negatively regulates tuber initiation by restricting the competence of tuber differentiation in stolon tips [22]. Despite these studies demonstrating the positive effects of JAs on tuberization, the information available on the molecular mechanism of tuber development regulated by JAs is still limited.
Proteomics approach as a powerful tool has identified some key proteins involved in potato tuber development [23][24][25]. Some phytohormone-responsive protein expression profiles of tuber development have also been reported in our recent research [26][27][28]. However, the information available on the protein expression profiles of tuber development regulated by JAs is still unknown. In the present study, the iTRAQ-based proteomic analysis was applied to investigate the molecular events of potato tuber development in vitro regulated by exogenous JA. This approach allows the simultaneous identification and quantitative comparison of peptides using tandem mass spectrometry (MS/MS) spectra. A comprehensive inventory of JA-responsive proteins involved in tuber development was established, which would provide new insights for the underlying molecular mechanism of potato tuber development regulated by JA.

Plant materials, exogenous JA treatments and in vitro tuberization
In vitro plantlets of potato (Solanum tuberosum L. cv. Atlantic) as the experimental materials were propagated from single-node sections on the basal MS medium containing 3% (w/v) sucrose and 0.8% (w/v) agar (pH5. 8), and cultured at 25 ± 2℃ under 16 h photoperiod (100 μmol m −2 s −1 light intensity) for four weeks [29]. The stolon induction from single-node explants and the tuber induction form stolon cuttings were described previously [28]. The tuber-induction MS medium was supplemented with different concentrations of exogenous JA (0, 0.5, 5 and 50 μΜ), respectively. After 40 days culture, the uniformly grown tubers were carefully harvested. The number of tubers per stolon was recorded from control and exogenous JA treatments, and the tuber size and weight were also measured. The fresh samples were used for physiological and biochemical analysis. The samples frozen in liquid nitrogen were stored at -80℃ for proteomic analysis.

Starch, reducing sugar and sucrose content assay
Starch and sugars were extracted as described by Matsuura-Endo et al. [30]. Approximately 2 g of fresh tubers was homogenized and incubated with 20 mL 80% (v/v) ethanol at 70℃ for 3 h. After centrifugated at 10,000 g for 20 min, the supernatant was collected, vacuumdried, dissolved in deionized water and passed through a membrane filter (0.2-μm, Millipore). The concentrations of fructose, glucose and sucrose in the filtrate were determined by HPLC (Model 1100 series, Agilent Technologies) with an Amide-80 column (HW-40F, TSKgel). Meanwhile, the pellet was vacuum-dried and added into 52% perchloric acid. The starch dissolved in perchloric acid was reacted with iodine solution, and then recorded the absorbance at 660 nm. The starch, reducing sugar and sucrose content were calculated on a fresh weight basis (mg g −1 FW).
H 2 O 2 content assay H 2 O 2 content was determined according to the method of Veljovic-Jovanovic et al. [31]. Approximately 0.5 g of fresh tubers was homogenized in liquid nitrogen with 2 mL of extraction solution [1 M HClO 4 and 5% (w/v) polyvinyl pyrrolidone (PVP)]. The homogenate was centrifuged at 12,000 g for 10 min at 4℃. The supernatant was neutralized with 5 M K 2 CO 3 in the presence of 100 mL 0.3 M phosphate buffer (pH5.6). The solution was then centrifuged at 12,000 g for 2 min at 4℃, and the sample was incubated for 10 min with 1 U ascorbate oxidase (Sigma). The reaction mixture consisted of 200 mL sample, 0.1 M phosphate buffer (pH6.5), 3.3 mM 3-dimethylaminobenzoic acid (DMAB) (Sigma), 0.07 mM 3-methyl, 2-benzo thiazolinone hydrazone (MBTH) (Sigma) and 0.3 U peroxidase (Sigma). The absorbance change was recorded at 590 nm, and the H 2 O 2 content were calculated on a fresh weight basis (μM g −1 FW).

Antioxidant enzyme activity assay
Approximately 2 g of fresh tubers was homogenized in liquid nitrogen with 10 mL of chilled extraction buffer [50 mM K-phosphate buffer (pH7.8), 1 mM Na-EDTA and 1% (w/v) PVP]. The homogenate was centrifuged at 15,000 g for 20 min at 4℃, and the supernatant was used for enzyme assay. All the steps in the preparation of enzyme extracts were performed at 4℃. Superoxide dismutase (SOD) activity was measured by nitroblue tetrazolium (NBT) method of Beyer and Fridovich and expressed as units mg −1 protein [32]. Ascorbate peroxidase (APX) activity was assayed by recording spectrophotometrically the decrease in ascorbate content at 290 nm (E = 2.47 mM −1 cm −1 ) according to the method of Ushimaru et al. [33] and expressed as units mg −1 protein. Catalase (CAT) activity was assayed by monitoring the consumption of H 2 O 2 at 240 nm (E = 39.4 mM −1 cm −1 ) according to the method of Aebi [34] and expressed as units mg −1 protein.

Protein extraction, digestion and iTRAQ labeling
Total tuber protein was extracted using an improved two-step precipitation method as described by Koistinen et al. [35]. Approximately 400 mg of frozen tuber samples were ground to a fine powder in liquid nitrogen. The powder was incubated with 1 mL of extraction buffer [50 mM Tris-HCl (pH8.0), 25 mM EDTA, 500 mM thiourea] containing 0.5% (v/v) β-mercaptoethanol for 30 min at 4 °C. Then, the homogenate was centrifuged at 13,000 g for 15 min at 4℃ to collect supernatants. The supernatants were mixed with Tris-phenol (pH8.0), and then centrifuged at 7,000 g for 10 min at 4 °C to collect phenol phase. The phenol phase was vortex mixed with five-fold volumes of 0.1 M cold ammonium acetatemethanol buffer and precipitated at -20 °C overnight. The samples were centrifuged at 12,000 g for 10 min at 4 °C to collect precipitation. Then, the pellet was washed with cold methanol, and centrifuged at 12,000 g for 10 min at 4 °C to collect precipitation. The wash step was repeated twice with acetone to remove methanol. The precipitation was air dried at room temperature for 5 min, and dissolved in lysis buffer [0.7 M urea, 2 M Thiourea, 2% (w/v) CHAPS, 2% (w/v) DTT] for 3 h. Finally, the samples were centrifuged at 12,000 g for 10 min at 4 °C to collect supernatants. Protein concentration was determined by BCA assay and stored at -80 °C. Protein digestion was performed according to the filter-aided sample preparation (FASP) procedure [36]. The digested peptides were labeled with iTRAQ Reagent-4 plex Multiplex Kit (SCIEX, USA). Four samples were labeled with iTRAQ tags 113 (0 μΜ JA), 114 (0.5 μΜ JA), 115 (5 μΜ JA) and 116 (50 μΜ JA) for three biological replicates. The labeled peptide mixtures were desalted by Agilent Zorbax Extend-C18 column and vacuum-dried.

LC-MS/MS analysis
Each labeled peptide was dissolved with 2% acetonitrile (ACN) containing 0.1% formic acid (FA). The online chromatography separation was performed on an Eksigent nanoLC 415 system (SCIEX, USA) using ChromXP C 18 column (3 μm, 75 μm × 15 cm, 120 A, ChromXP, Eksigent). The flow rate was 300 nL/min, and the linear gradient was 90 min (Mobile phase A: 2% ACN/0.1% FA, Mobile B: 95% ACN/0.1% FA). A Triple TOF 6600 tandem mass spectrometer (SCIEX, USA) was applied in MS analysis of the separated fractions. According to Zhu et al. [37], the data were acquired with a 2.4 kV ion spray voltage, 35 psi curtain gas, 12 psi nebulizer gas, and an interface heater temperature of 150 °C. The MS was scanned in IDA (Information-dependent acquisition) mode with a mass range 400-1,500 and an accumulation time of 250 ms. In each IDA cycle, 40 MS/MS spectra (80 ms, mass range 100-1,500) exceeding a threshold of 260 cps with a charge state of 2-4 were acquired. A rolling collision energy setting was applied to all precursor ions for collision-induced dissociation (CID), and the dynamic exclusion time was set for 16 s.

Database searching and protein quantification
iTRAQ MS/MS data were analyzed using ProteinPilot software v.5.0 (SCIEX, USA) for protein identification and quantification [37]. Database searching for each sample were performed in the Solanum tuberosum database of Universal Protein Resource (UniProt). Only the proteins identified at FDR (false discovery rate) ≤ 0.01 and unique peptides ≥ 1 were considered for protein lists and further analysis. The detected protein threshold in the software was set to achieve 95% confidence. For protein quantification, the DEPs were identified on the basis of the ratios of differently labeled proteins. The proteomic analysis was performed with biological triplicates, and t-test was used to evaluate the significance of DEPs between groups at p < 0.05. Proteins with fold change > 1.2 or < 0.83-fold change and p < 0.05 were considered as significant DEPs.

Bioinformatics analysis
The DEPs were functionally categorized according to GO annotation by BLAST2GO software [38]. The KEGG database (http:// www. genome. jp/ kegg/ pathw ay. html) was employed to analyze the canonical biochemical pathways [39]. A hypergeometric test was used to find the significantly enriched GO terms and KEGG pathways (p < 0.05) of DEPs. Protein-protein interaction (PPI) networks were constructed by search against Interaction Genes/Proteins (STRING) database [40], and the interaction network was illustrated by Cytoscape software. The "factoextra", "complexheatmap" and "vioplot" package in R language (Version 3.6.3) were used to generate the hierarchical clustering, heatmaps and violin plots of DEPs.

Immunoblot analysis
Total protein (20 μg) was loaded on 10% TGX Stain-Free FastCast acrylamide gel electrophoresis (Bio-Rad, USA) at 80 V for 30 min and at 120 V for 70 min in a Mini-PROTEAN Tetra chamber (Bio-Rad, USA). Protein loading was determined with Chemidoc XRS system (Bio-Rad, USA). Subsequently, proteins were transferred to a 0.45 µm Immobilon-FL PVDF membrane (Merck Millipore, USA) at 70 V for 60 min, and sealed with PBST buffer (137 mM NaCl, 2.7 mM KCl, 10 mM Na 2 HPO 4 , 2 mM KH 2 PO 4 , 0.1% Tween 20) containing 5% (w/v) nonfat milk for 1 h. In order to save antibody and chromogenic reagent, we cut the membrane into strips using the molecular weight standard as a guide after transfer to a PVDF membrane. The primary antibodies (PhytoAb, USA) of HSP90, LOX2 and COI1 were used at 1:1000 dilutions for 1 h, and the secondary antibody HRP-linked Goat anti-mouse IgG (PhytoAb, USA) and HRP-linked Goat anti-rabbit IgG (PhytoAb, USA) were also used at 1:1000 dilutions for 1 h. Western Lightning ™ Chemiluminescence Reagent Plus (Perkinelmer, USA) was adopted for color rendering, and the gel imaging system was employed for imaging. Protein expression was performed using Image Lab 6.1 software as the ratio of the tested proteins to total protein.

Statistical analysis
Statistical analysis was carried out with three biological replicates for physiological and proteomic analyses. The repeated measurement was given as means ± standard error (SE). For the data of physiological analyses, the significant differences were analyzed by Duncan's multiple range test (p < 0.05). For the data of proteomic analyses, t-test was used to evaluate the significance of DEPs between two groups, and one-way analysis of variance (ANOVA) followed by Bonferroni correction was performed for the multiple comparisons of DEPs. The p-values and corrected p-values (P adj ) < 0.05 were considered to be statistically significant.

Effects of exogenous JA on tuber tuberization in vitro
There were visible morphological changes of tubers in vitro treated by exogenous JA (Fig. 1). The low JA concentration (0.5 and 5 μM) promoted tuber development, whereas the high JA concentration (50 μM) showed inhibition effect. The tuber size was obviously increased under 5 μM JA treatment (Fig. 1A). Compared with control, the tuberization per stolon was significantly increased under 0.5 and 5 μM JA treatments, and there was no significant change under 50 μM JA treatment ( Fig. 1B). At the end of culture period (40 th d), the tuberization per stolon of 0.5 and 5 μM JA treatments were 1.55 and 2.68-folds than that of control, respectively. The tuber diameter was significantly increased by 31.13% and 51.76% under 0.5 and 5 μM JA treatments, and decreased by 58.44% under 50 μM JA treatment (Fig. 1C), respectively. Similarly, the fresh weight of tubers was significantly increased by 44.46% and 120.31% under 0.5 and 5 μM JA treatments, and decreased by 67.42% under 50 μM JA treatment (Fig. 1D), respectively. Compared with control, there was no significant change in dry weight of tubers under 0.5 μM JA treatment, whereas it was significantly increased by 107.25% under 5 μM JA treatment, and decreased by 68.63% under 50 μM JA treatment (Fig. 1E).

Physiological changes induced by exogenous JA of tubers in vitro
Exogenous JA caused a series of physiological changes of tubers in vitro. The starch content was significantly increased by 45.12% and 89.62% under 0.5 and 5 μM JA treatments, and then obviously decreased under 50 μM JA treatment ( Fig. 2A). With the increase of exogenous JA concentration, the reducing sugars content was significantly reduced by 13.72%, 25.66% and 69.47% under 0.5, 5 and 50 μM JA treatments (Fig. 2B), respectively. Compared with control, there was no significant change in sucrose content under 0.5 μM JA treatment, whereas it was significantly increased by 40.67% under 5 μM JA treatment, and decreased by 63.08% under 50 μM JA treatment (Fig. 2C). The activity of several antioxidant enzymes also showed obvious changes in tubers. With the increase of exogenous JA concentration, the H 2 O 2 content was significantly increased by 21.89%, 26.23% and 49.70% under 0.5, 5 and 50 μM JA treatments (Fig. 3A), respectively. The SOD activity was also significantly increased with the increase of exogenous JA concentration (Fig. 3B). The APX activity was significantly increased under 0.5 and 5 μM JA treatments (Fig. 3C). The CAT activity was significantly increased under 5 and 50 μM JA treatments (Fig. 3D).

Quantitative identification of JA-responsive DEPs with iTRAQ
A total of 2341 proteins were identified using iTRAQ analysis, and 2277 proteins were quantitatively analysed (Additional file 1: Table S1). According to Peptide FDR (false discovery rate) ≤ 0.01 and unique peptide number of protein ≥ 1, a total of 1157 proteins appearing simultaneously in three biological replicates were considered as the finally identified credible proteins (Additional file 2: Table S2). On the basis of the finally identified credible proteins, a total of 257 DEPs induced by JA during tuber development were determined according to fold change ≥ 1.2 or ≤ 0.83 and p < 0.05. Among these DEPs, 160 DEPs were identified between 0.5 μM JA treatments and control, including 21 up-and 139 down-regulated proteins (Additional file 3: Table S3-1). One hundred and fifteen DEPs were identified between 5 μM JA treatments and control, including 77 up-and 38 down-regulated proteins (Additional file 3: Table S3-2). Two hundred and ten DEPs were identified between 50 μM JA treatments and control, including 73 up-and 137 down-regulated proteins (Additional file 3: Table S3-3). One hundred and ninety-two DEPs were identified between 5 and 0.5 μM JA treatments, including 171 up-and 21 down-regulated  Table S3-4). One hundred and thirty-six DEPs were identified between 50 and 0.5 μM JA treatments, including 96 up-and 40 down-regulated proteins (Additional file 3: Table S3-5). One hundred and seventy-two DEPs were identified between 50 and 5 μM JA treatments, including 47 up-and 125 down-regulated proteins (Additional file 3: Table S3-6).
Furthermore, the 257 DEPs were functionally classified according to Uniprot, KEGG and eggNOG database information (Fig. 4A). They were divided into nine function categories, including metabolism (Pm, 39%), protein biosynthesis and degradation (Pb, 19%), transcription and translation (Pt, 13%), cellular defense (Pd, 10%), cell cycle and structure (Pa, 5%), transport (Pr, 5%), signaling (Pg, 4%), miscellaneous (Po, 4%) and unknown (Pu, 1%). Furthermore, the differential expression patterns of these proteins in different functional categories were analyzed (Fig. 4B), and all the protein names and information of heatmap were listed in the Additional file 4: Table S4. Under 0.5 μM JA treatment, most of DEPs in different functional categories were down-regulated. Under 5 μM JA treatment, the up-regulated proteins were more than down-regulated proteins in different functional categories except for protein synthesis and degradation-related proteins (Pb). Under 50 μM JA treatment, most of the transcription and translation-related proteins (Pt) and signaling-related proteins (Pg) were down-regulated, whereas the protein biosynthesis and degradation-related proteins (Pb) were up-regulated. Additionally, the distribution patterns of DEP expression levels under different JA concentrations were also investigated (Fig. 4C). The expression distribution of cellular defense-related proteins (Pd), signaling-related proteins (Pg), metabolism-related proteins (Pm) and transcription and translation-related proteins (Pt) were relatively concentrated, which were mainly up-regulated under 5 μM JA and down-regulated under 0.5

Cluster analysis of JA-responsive DEPs during tuber development
A K-median clustering analysis was applied to JA-responsive DEPs during tuber development (Fig. 5). The optimal number of clusters was chosen by maximizing the Calinski-Harabasz index (Calinski) of DEPs, which indicated that it was best to be divided into two K-median clusters (Fig. 5A). Furthermore, the principal component analysis (PCA) showed that the distribution of DEPs in both two clusters was concentrated, and the concentration degree of cluster 2 is better than that of cluster 1 (Fig. 5B). In cluster 1, the expression trend of DEPs showed a gradual up-regulation with the increase of exogenous JA concentration (Fig. 5C). The protein functions contained in cluster 1 were listed by the descending order of protein number as follows: metabolism (Pm), protein biosynthesis and degradation (Pb), cellular defense (Pd), miscellaneous (Po), cell cycle and structure  The red area represents cluster 1, and the blue area represents cluster 2. C All the DEPs were grouped into two clusters based on the similar expression pattern according to K-median clustering. D The function categories of DEPs in each cluster with similar expression pattern. Pm, metabolism; Pb, protein biosynthesis and degradation; Pt, transcription and translation; Pd, cellular defense; Pg, signaling; Pr, transport, Pa, cell cycle and structure; Po, miscellaneous; Pu, unknown transport (Pr), signaling (Pg), miscellaneous (Po) and unknown (Pu) (Fig. 5D).

Functional enrichment analysis of JA-responsive DEPs during tuber development
GO categorization and KEGG pathway annotation were used to display the functional enrichment of JAresponsive DEPs (Fig. 6). All the DEPs were categorized into three GO terms including biological process, cellular component and molecular function (Fig. 6A). In total, 22 GO categories were significantly enriched in three levels, including 11 GO terms in biological process, 2 GO terms in cellular component and 9 GO terms in molecular function. In terms of biological processes, the top five enriched GO terms were Negative regulation of peptidase activity (GO:0010466), Small molecule metabolic process (GO:0044281), Cellular response to toxic substance (GO:0097237), Protein folding (GO:0006457) and Reactive oxygen species metabolic process (GO:0072593), respectively. In terms of cell composition, the enriched GO terms were Cytoplasm (GO:0005737) and Extracellular region (GO:0005576), respectively. In terms of molecular function, the top five enriched GO terms were Peptidase inhibitor activity (GO:0030414), Antioxidant activity (GO:0016209), Hydro-lyase activity (GO:0016836), Structural molecule Fig. 6 The GO term and KEGG pathway enrichment of JA-responsive DEPs during tuber development in vitro. A The GO categorization of DEPs. Three GO sources (BP, biological process; CC, cellular component; MF, molecular function) were represented by red, blue, and green spots, respectively. B The KEGG pathway annotation of DEPs. The number in the spot represents protein number activity (GO:0005198) and Unfolded protein binding (GO:0051082), respectively. The KEGG pathway enrichment analysis indicated that the JA-responsive DEPs during tuber development were mainly enriched in 15 pathways (Fig. 6B). The top five enriched pathways were mainly focus on "Metabolic pathways", "Pyruvate metabolism", "Ribosome", "Carbon metabolism" and "Biosynthesis of secondary metabolites".

Protein-protein interaction analysis of JA-responsive DEPs during tuber development
The JA-responsive DEPs were analyzed using the STRING online search tool to build an interaction network of DEPs (Fig. 7). The Maximal Clique Centrality (MCC) algorithm was used to analyze the key node proteins in the interaction network (Fig. 7A). The top ten key node proteins were 40S Ribosomal protein S15A-1 (Q3HRZ3), 60S Ribosomal protein L4/L1 (M1ARJ5), Ribosomal protein L3 (Q2VCJ2), 60S Ribosomal protein L7A (Q2XPW4), 40S Ribosomal protein S24 (M1BPE5), 60S Ribosomal protein L8 (M1BZ12), 60S Ribosomal protein L18 (M1CAV1), 60S Ribosomal protein L6 (M1B0U4), Glycoprotein (M1ANH5) and Ribosomal protein (M1A5C6), respectively. Furthermore, the functional categories of interacting proteins were analyzed (Fig. 7B). The corresponding functional categories of interacting proteins were represented by the ribbons with different colors in a chord diagram, which mainly involved in translation (Tl), transcription (Ts), carbohydrate metabolism (Mc), amino acid metabolism (Ma), energy metabolism (Me), phospholipid metabolism (Ml), inorganic phosphate ion metabolism (Mp), JA metabolism (Mj), nucleotide metabolism (Mu), other metabolism (Mo), proteases and peptidases (Pe), protein folding (Pc), transport and channel (Pr), signaling (Pg), cellular structure (Pa), defense (Pd), protease inhibitors (Pi), storage proteins (Ps) and other proteins (Po). Among them, the proteins with relatively high interaction frequency were mainly involved in translation (Tl), protein folding (Pc), transport and channel (Pr), signaling (Pg), defense (Pd), transcription (Ts) and energy metabolism (Me) (Fig. 7B). Additionally, we also separated and visualized the positive and negative correlations among these functional categories of interacting proteins using a chord diagram (Fig. 7C). The thickness of chords illustrated the relative contribution of individual correlation coefficients to the global correlation. The ribbons within the circle correspond to significant correlations with a p < 0.05, and the red and blue ribbons indicated positive and negative coefficients, respectively. The data showed that there existed a universal positive correlation almost among the interacting proteins with all the above functional categories. For example, the translation (Tl)-related proteins were strongly correlated positively with the proteins involved in transcription (Ts), protein folding (Pc) and signaling (Pg). The carbohydrate metabolism (Mc)related proteins were strongly correlated positively with the proteins involved in nucleotide metabolism (Mu) and amino acid metabolism (Ma). Negative correlations were observed only between the interacting proteins involved in translation (Tl) and nucleotide metabolism (Mu), nucleotide metabolism (Mu) and amino acid metabolism (Ma), translation (Tl) and energy metabolism (Me).

Immunoblot analysis of key proteins in JA biosynthesis and regulatory pathway during tuber development
The expression patterns of Lipoxygenase 2 (LOX2), Coronatine insensitive 1 (COI1) and COI1/JAZ binding accessory protein (HSP90) in JA biosynthesis and regulatory pathway were determined during tuber development by immunoblot analysis (Fig. 8; Additional files 5, 6, 7 and 8: Fig. S1-4). Compared with control, the expression level of COI1 was increased by 71.3% and 69.7% under 0.5 and 5 μM JA treatments, and no expression was detected under 50 μM JA treatment (Fig. 8A). The expression level of HSP90 was increased by 12.0%, 34.5% and 65.5% under 0.5, 0.5 and 5 μM JA treatment than that of control (Fig. 8B). The expression level of LOX2 was reduced by 61.8% and 59.3% under 0.5 and 5 μM JA treatment than that of control, whereas slightly increased under 50 μM JA treatment (Fig. 8C). The immunoblot analysis results of LOX2 and HSP90 were consistent with proteome results.

Discussion
Potato tuber development is a complex biological process that requires the coordinated interaction of endogenous and external factors [4]. Phytohormones play crucial roles in regulating morphological events of potato development [8]. It is well known that JAs are a kind of phytohormones with multiple functions in plants [10]. JAs are mainly involved in pathogen defense in many higher plants, whereas JAs has a specific role of tuber-inducing activities in potato [11,14]. Numerous studies have focused on the favorable effects of JAs on tuber development [14,17,18]. However, the underlying molecular mechanism of tuber development regulated by JAs is still largely unknown. The present study attempted to integrate physiological and proteomic approach to illuminate the JA-signaling response mechanism of potato tuber development in vitro.

The cell wall and cytoskeleton composition were regulated by JA to promote tuber cell expansion
Both the cell division and cell enlargement contribute to potato tuber development [41]. The changes of cell wall, plasma membrane and cytoskeleton can alter cell morphology [42]. Previous study has shown that JA can Data are presented as means ± SE for three independent experiments. In order to save antibody and chromogenic reagent, the membranes were cut into strips using the molecular weight standard as a guide after transfer to a PVDF membrane. The first membrane was cut just 1 cm above and below the 70 KDa molecular weight marker and used for COI1 immunoblot. The second membrane was cut just between the 70 KDa and 100 KDa molecular weight markers and used for HSP90 immunoblot. The third membrane was cut just 1 cm above and below the 100 KDa molecular weight marker and used for LOX2 immunoblot. The original gels and blot strips are presented in Additional files 5, 6, 7 and 8: Fig. S1-4 induce cell wall remodeling by sensing the osmotic pressure changes of cells [43]. In the present study, some cell wall synthesis-related proteins were found to be regulated by JA during potato tuber development. It appeared that JA might regulate the cell wall polysaccharide synthesis and the raw material transportation of cell wall biosynthesis, so as to provide a material basis for tuber expansion (Fig. 9). In the plant cell wall polysaccharide synthesis pathway, UDP-glucose 4-epimerase (UGE4) catalyzes the transformation between UDP-glucose and UDP-galactose [44]. The inhibition of UGE4 expression can alter the arrangement of plant root cells [44]. The down-regulation of alpha-1,4-glucan-protein synthase (UAM) can reduce xylan content in cell wall, and the decrease of xylan content in cell wall lead to cell enlargement [45,46]. In this study, the UAM (M1C4C1) was significantly down-regulated (p < 0.05) and UGE4 (M1BN71) was slightly down-regulated under 0.5 and 5 μM JA treatment, whereas they were both significantly up-regulated (p < 0.05) under 50 μM JA treatment. It appeared that low JA concentration might promote tuber cell enlargement by regulating the composition of polysaccharides in cell wall during tuber development. Annexin and Vesicle transport v-SNARE involved in vacuole secretion and Golgi transport can affect cell wall formation by regulating cell wall polysaccharide transport [47,48]. JA can enhance ANNEXIN expression, thus inhibiting glucan synthase activity [49,50]. When pathogens invaded, the increased JA level can induce v-SNARE expression to accelerate polysaccharide deposition in cell wall and enhance cell wall strength [51,52]. The v-SNARE (M1BPY2) was significantly up-regulated (p < 0.05) under 5 μM JA treatment, and the Annexin D4 (M1BPR3) showed a slight up-regulation. It was suggested that JA might also promote cell wall remodeling by accelerating the transport of cell wall synthesis elements during tuber development.
In addition, plant cell enlargement is accompanied cytoskeleton composition change, mainly the change of microtubule and microfilament polymerization status Fig. 9 The JA-responsive DEPs involved in cell wall and cytoskeleton composition during tuber development in vitro. The heat-map presented the expression change of these DEPs. The significance of t-test was presented by "*" (p < 0.05). The green band indicated the corrected p-value (P adj < 0.05, one-way ANOVA analysis of variance followed by Bonferroni correction for multiple comparison) was mapped as an annotation of heatmap. ADF, actin depolymerizing factor; MAP65-1a, microtubule-associated protein 65-1a; SnRK2.4, SNF1-releted protein kinases 2.4; SPS, sucrose-phosphate synthase; TCTP, translationally-controlled tumor protein; UAM, alpha-1,4-glucan-protein synthase; UGE4, UDP-glucose 4-epimerase; V-SNARE, soluble N-ethylmaleimide-sensitive fusion protein attachment protein receptors [42]. The microtubule of eukaryotic cells is composed of α and β-Tubulin heterodimer [53]. Microtubule as the core element of cytoskeleton plays a key role in cell migration, mitosis, cell polarity, intracellular transport and cell morphogenesis [54,55]. It undergoes depolymerization and rearrangement in the process of cell enlargement, which is a depolymerization status in the active meristem [56]. The abnormal expression of α-Tubulin gene can cause the disorder of microtubule structure in Arabidopsis root tip cells, which affects the normal cell division of root tips [57,58]. The depolymerization of Actin protein in cytoskeleton assembly microfilaments can inhibit plant apical tissue growth, and this biological process is performed by Actin depolymerizing factor (ADF) [59]. Cytoskeleton depolymerization was also observed when JA promoted potato tuber cell enlargement [16], and this dynamic change was conducive to tuber cell enlargement [60,61]. The finding here that α-Tubulin (M0ZYR0), β-Tubulin (B5M4B1) and ADF (M0ZV99) were significantly down-regulated (p < 0.05) under 50 μM JA treatment. It was suggested that the high JA concentration might restrict the dynamic structure changes of cytoskeleton by inhibiting the expression of cytoskeleton-related proteins, which was not conducive to tuber cell enlargement during tuber development.

The primary carbon metabolism was remodeled by JA to provide metabolism intermediates and energy for tuber development
The primary carbon metabolism can produce precursors required for secondary metabolism and generate energy, which makes the fixed carbon flow between energy storage and consumption, thus affecting plant growth and development [62]. Some primary carbon metabolism-related enzymes were identified during potato tuber development regulated by JA. The expression pattern changes of these proteins were suggested that the primary carbon metabolism were remodeled by JA to meet the increased substance and energy requirement for tuber development (Fig. 10). Glycolysis is the core of bioenergy conversion that provides carbon skeleton for various metabolite biosynthesis [63]. Pyrophosphate: fructose 6-phosphate phosphotransferase (PFP) is the rate limiting enzyme in glycolysis pathway, which catalyzes the reversible transformation between fructose 6-phosphate (F6P) and fructose 1,6-diphosphate (FBP) [64]. Previous study has shown that the down-regulation of PFP expression leads to the increase of hexose phosphate pool in potato tubers, thus weakening starch synthesis [65]. Pyruvate kinase (PK) catalyzes the final reaction of glycolysis pathway to produce pyruvate that enters mitochondria as a substrate for respiration [66]. The decreased PK activity in potato tubers leads to a decrease of pyruvate and organic acid levels in tricarboxylic acid (TCA) cycle, accompanying by the carbon source distribution between glycolysis and starch [67]. The finding here that PFP (P21343) and PK (M1AQZ1) were significantly down-regulated (p < 0.05) under 0.5 and 50 μM JA treatments, which was suggested that the sufficient carbon source and energy cannot be supply for tuber development due to the decreased glycolysis rate. Pyruvate dehydrogenase (PDH) as a key enzyme in TCA cycle catalyzes the oxidative decarboxylation of pyruvate to form acetyl CoA [68]. Previous study has shown that the down-regulation of PDH leads to the abnormal development of tobacco flowers [69]. ATP citrate synthase (ACLY) catalyzes the conversion of acetyl CoA to citric acid in TCA cycle, and plays an important role in flower development, nutrient absorption and carbon skeleton source for nitrogen assimilation [70]. Isocitrate dehydrogenase (IDH) is considered to be a key regulatory node in TCA cycle, and plays important roles in maintaining 2-oxoglutarate level and regulating nitrogen assimilation [63]. The ACLY (M1A0G4) and IDH (M1D530) were found to be significantly down-regulated (p < 0.05) under 0.5 μM JA treatments, slightly down-regulated under 50 μM JA treatment, whereas significantly upregulated (p < 0.05) under 5 μM JA treatment. The PDH (M1AZL8) also showed a significantly down-regulation expression (p < 0.05) under 0.5 and 50 μM JA treatment. It appeared that a certain JA concentration might accelerate TCA cycle to provide sufficient energy and substance for tuber development.
The reprogramming of protein biosynthesis, degradation and assembly was required for tuber development regulated by JA The precise regulation of protein metabolism plays crucial roles in numerous developmental and physiological processes in plants. Recent studies have established a certain connection between some key genes participating in various steps of cellular protein metabolism and phytohormone signal transduction [30,76]. A large number of protein metabolism-related proteins were also identified in the present study, which was mainly involved in pre-mRNA processing and translation, protein biosynthesis, degradation and assembly (Fig. 11). It appeared that the reprogramming of protein biosynthesis, degradation and assembly might be required for potato tuber development regulated by JA. RNA splicing is an essential process to produce mature mRNA in eukaryotes [77]. Heterogeneous nuclear ribonucleoproteins (HnRNPs) as a large family of RNA binding proteins play multiple roles in pre-mRNA splicing, transcription, translation and turnover [78,79], which are involved in the regulation of flower development, circadian rhythms, hormone signaling, stress response and phloem transport in plants [80][81][82]. DEAD-box ATP-dependent RNA helicases is responsible for the entry of nuclear pre-mRNA into cytoplasm for splicing and pre-translational processing [83]. MAR-binding protein participates in the composition of dense fibrous complex (DFC) in plant nucleolus, and the DFC contains factors are involved in pre-RNA processing [84]. The HnRNP G (M1CWV3), HnRNP A1 (M1BGT4) and DEAD-box ATP-dependent RNA helicases UAP56 (M1BTY2), MAR-binding protein NOP58 (M1A6C0) were found to be significantly up-regulated (p < 0.05) under 5 μM JA treatment, which might promote protein translation by improving the efficiency of pre-mRNA splicing and mRNA transport during tuber development.
The protein biosynthesis and transport is one of the key determinants for the rapid division of cells [85]. Ribosomal proteins (RPs) are a kind of highly conserved proteins that make up ribosomal subunits involved in the cellular process of translation [86]. The expression of RPs is higher in rapidly dividing cells, which can be stimulated by environment and growth factors [87,88]. Knockout of AtRPL23aA resulted in the slow growth and reduced fertility of Arabidopsis plants [89]. In this study, several RPs including RP large subunit (Q2VCJ2, M1CDK5, M1CAV1, M1A5C6 and Q2XPW4) and RP small subunit (M1CBF6, Q38HT5 and M1BPE5) were significantly down-regulated (p < 0.05) under 0.5 and 50 μM JA treatments. It might hinder mRNA/ribosome interactions early in translation, which was not conducive to tuber development. Ran is a kind of small GTPase that plays a pivotal role in mitotic spindle assembly, nuclear envelope assembly and protein transport from nucleus to cytoplasm [90] Overexpression of TaRAN1 in Arabidopsis and rice can increase the proportion of cells in G2 phase of cell cycle, and lead to an elevated mitotic index and prolonged life cycle [91]. Ran (Q38JH1) was found to be significantly down-regulated (p < 0.05) under 5 μM JA treatment, which might promote tuber development by accelerating protein transport and promoting tuber cell division. Secretion-associated RAS superfamily (SAR) is involved in protein transport from endoplasmic reticulum (ER) to Golgi apparatus by a coat protein complex II (COPII)mediated pathway [92]. Simultaneous knockdown of OsSar1 in rice endosperm can prevent the transport of glutenin and α-globulin from ER to Golgi apparatus, resulting in floury and shrunken seeds [93]. SAR1 (Q3HRY1) was also found to be significantly down-regulated (p < 0.05) under 5 μM JA treatment, which might promote tuber storage proteins exiting from ER and accumulating during tuber development.
Proteolysis is necessary for the removal of abnormal, modified and mistargeted proteins, and altering the balance of proteins [94]. Several proteasome components including RPN1 (M1AJV5), RPT2 (M1BS59) and PSMB6 (M1ALA3) were found to be significantly down-regulated (p < 0.05) under 5 μM JA treatment, which might play key roles in maintaining strict protein quality control and degrading specific sets of proteins during tuber development. Chaperones are responsible for protein folding, assembly, translocation and degradation in many normal cellular processes [95]. Calnexin (CNX) acts as a molecular chaperone that plays multiple roles in Ca 2+ binding, lectin-like activity, and recognition and degradation of misfolded proteins [96]. DRP94 is a member of HSP90 protein family in ER that is involved in the regulation of Ca 2+ in cells, thus promoting the transport of endoplasmic reticulum proteins [97]. HYOU1 is a homologous protein of HSP70 family of chaperone proteins in ER that assists in protein folding, assembly and disassembly of protein complexes, pulling polypeptides from ribosomes and transmembrane pores, inactivating signaling proteins and controlling their degradation [98]. The CNX (M1B4H5), DRP94 (M1A6G8) and HYOU1 (M1BAZ0) were found to be significantly down-regulated (p < 0.05) Fig. 11 The JA-responsive DEPs involved in protein biosynthesis, degradation and assembly during tuber development in vitro. The heat-map presented the expression change of these DEPs. The significance of t-test was presented by "*" (p < 0.05). The green band indicated the corrected p-value (P adj < 0.05, one-way ANOVA analysis of variance followed by Bonferroni correction for multiple comparison) was mapped as an annotation of heatmap. BIP, luminal binding protein; CNX, calnexin; GRP94, glucose-regulated protein 94; EIF2, eukaryotic translation initiation factor 2; EIF6, eukaryotic translation initiation factor 6; HnRNP A1, heterogeneous nuclear ribonucleoprotein A1; HnRNP G, heterogeneous nuclear ribonucleoprotein G; HSP20, heat shock protein 20; HSP90A, heat shock protein 90A; HYOU1, hypoxia up-regulated protein 1; NOP58, MAR-binding protein NOP58; PSMB6, proteasome subunit β type 6; RAN, GTP-binding nuclear protein; RPN1, proteasome subunit RPN1; RPT2, proteasome subunit RPT2; RP-L3e, ribosomal protein L3e; RP-L7Ae, ribosomal protein L7Ae; RP-L10Ae, ribosomal protein L10Ae; RP-L17e, ribosomal protein L17e; RP-L18e, ribosomal protein L18e; RP-S2e, ribosomal protein S2e; RP-S23e, ribosomal protein S23e; RP-S24e, ribosomal protein S24e; SF3b3, splicing factor 3b subunit 3; UAP56, DEAD-box ATP-dependent RNA helicases; SAR1, secretion-associated RAS superfamily 1; DKC1, Dyskerin 1 under 0.5 μM JA treatment, which might reduce the degradation ability of denatured proteins and the transport of newly synthesized peptides during tuber development.

Conclusion
The present study first integrated physiological and proteomic analysis to investigate the molecular events of potato tuber development in vitro regulated by exogenous JA. The DEPs that play a variety of cellular functions were identified by iTRAQ-based proteomic analysis, which were involved in a dynamic network for tuber development. It indicated that the promotion effects of low JA concentration (especially 5 μM JA) on tuber development mainly exhibited in three major cellular processes (Figs. 9, 10 and 11). First, low JA concentration might promote tuber cell expansion by regulating the expression of cell wall polysaccharide synthesis and cytoskeleton formation-related proteins. Second, low JA concentration might cause the remodeling of carbon source distribution and energy flow to provide metabolism intermediates and energy for tuber development by regulating the expression of primary carbon metabolism-related enzymes. Third, low JA concentration might cause the reprogramming of protein biosynthesis, degradation and assembly to promote tuber protein biosynthesis and maintain strict protein quality control during tuber development. This study provided a comprehensive overview on the functional protein profile changes of tuber development regulated by JA.


Murine brain tumor microenvironment immunophenotyping using mass cytometry

Summary Here, we present a mass cytometry protocol optimized to examine the phenotype of immune cells within the mouse glioma microenvironment, using a Sleeping Beauty transposon-mediated mouse glioma model. We describe antibody conjugation and titrations for analysis of immune cells. We then detail mouse brain tumor tissue collection and processing, staining, followed by data acquisition, analysis, and gating strategy. This protocol can be applied to any brain tumor-harboring mouse model. For complete details on the use and execution of this protocol, please refer to Alghamri et al. (2021).

3. Upon completion of the 30 min antibody incubation, purify the partially reduced antibody.
a. Add 300 mL of C-Buffer to the 50 kDa filter to wash. b. Centrifuge at 12,000 3 g at 25 C for 10 min in a microcentrifuge and discard the flowthrough. c. Add 400 mL of C-Buffer to the 50 kDa filter. d. Wait 15-20 min to allow antibody and polymer prep timing to align in an upcoming step. e. Centrifuge the reduced antibody at 12,000 3 g at 25 C for 10 min in a microcentrifuge. 4. Perform the polymer wash.
a. Add 200 mL of L-Buffer and the 100 mL polymer mixture to a 3 kDa filter. b. Centrifuge at 12,000 3 g at 25 C for 25 min in a microcentrifuge and discard the flowthrough. This table lists the metal conjugated antibodies used for the immunophenotyping in this protocol. The metals listed in the table are Neodymium (Nd), Samarium (Sm), Europium (Eu), Gadolinium (Gd), Dysprosium (Dy), Ytterbium (Yb), Erbium (Er), Praseodymium (Pr), Terbium (Tb), Holmium (Ho), Thulium (Tm), Lutetium (Lu), Bismuth (Bi), and Yttrium (Y). a Indicates the antibodies that are not pre-conjugated, so these must be conjugated in-house from purified antibodies.
c. Add 400 mL of C-Buffer and centrifuge at 12,000 3 g at 25 C for 30 min in a microcentrifuge.
Note: The C-Buffer is a part of the Maxparâ X8 Antibody Labeling Kits specified in the key resources table. This buffer is used in this protocol without any further modification.
Note: To save time, step 4 can be performed simultaneously with steps 2 and 3.
5. Conjugate the antibody. a. Retrieve the 3 kDa and the 50 kDa filters from the microcentrifuges and discard the flowthroughs. b. To reach a final volume of approximately 80 mL, add 60 mL C-Buffer to the residual volume in the 3 kDa filter and resuspend the polymer by rinsing the filter walls. c. Transfer the approximately 80 mL polymer solution from the 3 kDa filter to corresponding antibody solution in the 50 kDa filter.
Note: Accounting for the residual volume in the 50 kDa filter, the final volume should now be approximately 100 mL.
d. Rinse the walls of the 50 kDa filter and mix the solution gently by pipetting. e. Incubate at 37 C in a water bath for 90 min. 6. Wash conjugated antibody.
a. Add 200 mL of W-Buffer to the 100 mL conjugated antibody mixture within the 50 kDa filter and mix gently by pipetting.
Note: The W-Buffer is a part of the Maxparâ X8 Antibody Labeling Kits specified in the key resources table. This buffer is used in this protocol without any further modification.
b. Centrifuge at 12,000 3 g at 25 C for 10 min in a microcentrifuge and discard the flow-through. c. Repeat steps 6a and 6b three more times. 7. Determine yield.
a. Add 80 mL of W-Buffer to the 50 kDa filter to resuspend the conjugated antibody to a final volume of 100 mL. b. Rinse the walls of the filter and gently mix the W-Buffer and conjugated antibody by pipetting approximately five timesusing a 200 mL pipette tip. c. Using W-Buffer as the blank solution, quantify yield using the NanoDropä by measuring absorbance at 280 nm.
Note: In addition to the absorbance, the NanoDropä will return a concentration in ng/mL. Given that the total solution should be in 100 mL, calculate the total mass of conjugated antibody. Expected yield is about 60%, which can be calculated by dividing the mass of conjugated antibody by the 100 mg that were initially added in step 2c. 8. Store conjugated antibody in its final solution. a. Centrifuge 50 kDa filter at 12,000 3 g at 25 C for 10 min in a microcentrifuge. b. Add the calculated volume of Antibody Stabilizer PBS supplemented with 0.05% sodium azide.
Note: Use approximately 20 mL less than calculated to account for the residual volume.
c. Invert the 50 kDa filter into a new collection tube and centrifuge at 1,000 3 g at 25 C for 2 min in a microcentrifuge. d. Label, seal with parafilm, and store the tubes with conjugated antibodies at 4 C.

Titration of antibodies and assessing signal spillover
Timing: 2-3 days Optimal generation of an antibody panel for mass cytometry requires antibody titrations and checking for signal spillover. The data from the titration experiment is analyzed to identify the optimal concentration, at which, there is a good separation between antibody-labeled positive and negative populations. The spillover test is used to confirm that there is minimal overlap between antibody channels, as this can lead to inaccurate results and false conclusions. 9. Prepare the surface antibody tubes for the spillover test.
a. Label spillover tubes for the spillover test (''Panel A 1:50'', ''Panel B 1:50'', ''Panel C 1:50''), which correlate with the antibodies to be added to them (see Table 2). b. In the spillover tube labeled ''Panel A'', add 36 mL of Maxparâ Cell Staining Buffer and 1 mL of each antibody indicated in the column. c. In the spillover tube labeled ''Panel B'', add 39 mL of Maxparâ Cell Staining Buffer and 1 mL of each antibody indicated in the column except CD16/32 and CD206. d. In the spillover tube labeled ''Panel C'', add 40 mL of Maxparâ Cell Staining Buffer and 1 mL of each antibody indicated in the column. 10. Prepare the surface antibody tubes for the titration test.
a. Label titration tubes for serial dilutions (''Surface 1:50'', ''Surface 1:100'', ''Surface 1:200'', ''Surface 1:400'', ''Surface 1:800''). b. In the ''Surface 1:50'' tube, add 30 mL of Maxparâ Cell Staining Buffer and 2 mL of each antibody indicated in the column except the highlighted CD16/32 and CD206. Mix well. c. Add 50 mL of Maxparâ Cell Staining Buffer to the four other titration tubes. d. Perform a serial dilution by adding and mixing 50 mL of the 1:50 tube to the 1:100 tube; then transfer 50 mL from the 1:100 tube to the 1:200 tube, and so on until the 1:800 tube is left with a final volume of 100 mL of surface antibody mix (See Figure 1)  Note: The Panel A and Panel C tubes will be receiving a normal Fc Block solution (consisting of anti-CD16/32), so this antibody will be prepared in the same tube in step 13b. The Panel B tube will be receiving the lanthanide-labeled antibody targeting CD16/32 to examine this The table outlines the metal conjugated antibodies to be added to each tube during the test for assessing signal spillover. The CD16/32 and CD206 (MMR) antibodies are stained at times seperate from the rest of the antibodies. The CD16/32 antibody will be used during the Fc block staining step of the other two tubes. The CD206 antibody is used during the intracellular staining step.

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molecule on the cells, since this molecule is a target in the panel, so it will be prepared separately in step 13c.
b. In the ''Panel A&C Fc Block'' tube, add 2 mL purified anti-CD16/32 antibody (1 mg/mL) to 98 mL of Maxparâ Cell Staining Buffer to make the 1:50 solution at 0.02 mg/mL. c. In the ''Panel B Fc Block'', add 1 mL of the lanthanide conjugated anti-CD16/32 antibody (0.5 mg/mL) to 49 mL of Maxparâ Cell Staining Buffer to make the 1:50 solution at 0.01 mg/mL. 14. Prepare the Fc receptor blocking solutions for the titration tubes. 16. Prepare a cell suspension of naïve wild type mouse splenocytes in a concentration of 3 3 10 6 cells/mL of media. 17. Add 1 mL of splenocyte solution into 8 polypropylene tubes. Discard the rest. 18. Label these tubes as the spillover tubes (Panel A-C) and titration tubes (Titration 1:50-1:800). 19. Spin down the splenocytes at 300 3 g for 5 min at 4 C. 20. Resuspend the cells at 2 3 10 7 cells/mL of media. 21. Follow the ''cisplatin (cell viability) staining'' section protocol below. 22. Centrifuge the cells at 300 3 g for 5 min at 4 C and discard the supernatant. 23. Resuspend the pellets in 50 mL of the Fc receptor blocking solution which corresponds to each tube. 24. Incubate at 25 C for 10 min. 25. Wash the cells.
a. Add 2 mL of Maxparâ Cell Staining Buffer. b. Centrifuge at 300 3 g at 4 C for 5 min. c. Discard supernatant. 26. Gently pipette to resuspend the pellet in 50 mL of the surface antibody mix which corresponds to each tube (see Table 3). 30. Follow the ''fixing and intracellular antibody staining'' section protocol below using the intracellular antibody mix which corresponds for each tube (see Table 3).
Note: Since spillover tubes for Panel A and Panel C do not have the stains for intracellular antibodies, add Maxparâ Perm S Buffer with no antibodies in place of intracellular antibody stain.

Follow the ''fresh fix and DNA intercalator staining'' section protocol below.
Pause point: The cells can be left up to 2 days at 4 C until acquisition.
32. Follow the ''sample acquisition on CyTOFâ instrument'' protocol below. 33. Follow the ''normalization'' section protocols below. 34. Using Cytobank, as described in the ''expected outcomes'' section, gate for the cell populations using the sample antibodies for each concentration.  Table 2). There is no spillover if the cells stained with each antibody panel are only positive for the antibodies they were stained for. If samples are identified as positive for antibodies that were not added in that panel's antibody mix, there is most likely spillover. In this case, you would need to use a lower concentration of antibodies that are of the same element and adjacent atomic weight within that panel.
Note: For example, there would be spillover if a sample stained with ''Panel B'' antibody mix has cells that are positive for 143Nd, since the 143Nd-labeled antibody is not present in ''Panel B'' (see Table 2). Thus, you would need to use a lower concentration of the 142Ndlabeled and 144Nd-labeled antibodies in the main experiment to minimize their spillover into the 143Nd channel, since the 142Nd-labeled and 144Nd-labeled antibodies in ''Panel B'' are spilling over into the 143Nd channel.
36. Compare the ability of the antibodies to bind and identify the positive cell population using the different antibody dilutions (See Figure 2). The lowest antibody concentration that allows for clearly discerning the positive and negative populations of the antibody-targeted molecule should be selected and used for the main experiment.
CRITICAL: Give close attention to choosing the optimal dilution for each antibody. This determines the antibody dilutions to use for the remainder of the protocol.

Processing the tissues into the single cell suspension
Timing: 2.5 h (this time is for processing 6 mouse samples) This step details the process of generating a single cell suspension from the glioma-harboring GEMM and isolating the immune cells for staining.
37. Prepare materials. a. Per sample label: one 50 mL conical tube; two 15 mL conical tubes; one 70 mm cell strainer and one cryovial. b. Place the cell strainer into the 50 mL conical tube. c. Wet the cell strainer by adding 2 mL of media. d. Insert the conical tubes into an ice bucket. 38. Euthanize mouse and begin tissue collection.
a. Inside a fume hood, add 1 mL isoflurane to a cotton pad. b. Place the isoflurane-soaked cotton pad to the bottom of the Nalgeneä Polypropylene Desiccator chamber and cover that area with the desiccator plate. quickly cover the desiccator to prevent vaporized isoflurane from escaping the desiccator chamber. c. Place the mouse into the chamber and cover the chamber. d. Once mouse stops all movement, wait for 20 more seconds. e. Confirm the mouse is euthanized by toe pinch and perform decapitation.
Note: For isoflurane euthanasia, any chamber that allows for administration of highly concentrated, vaporized isoflurane without the mouse coming into direct contact with the liquid Figure 2. Example selection of optimal concentration of antibodies from titration data The titration step is used to determine the best concentration of antibody for use in the staining process for the main experiment. This CD11b, CD162, and CD4 titration data is an example of the gates generated from these antibodies at different concentrations. The blue arrow indicates it as the selected concentration for further use in identifying the positive cell populations. The red and blue boxes in the selected graphs identify the positive and negative cell populations, respectively. isoflurane can be used in place of the Nalgeneä Polypropylene Desiccator. Additionally, alternative forms of euthanasia, such as a cervical dislocation, may be used in place of isoflurane overdose as long as they do not elicit alterations in the cell characteristics.
39. Process the tumor to generate a single cell suspension.
a. Dissect the brain and place it onto a petri dish with enough media to cover the surface of the dish. b. Dissect the tumor mass under a stereo-microscope and place it on the 70 micron strainer.
Note: The GEMM tumors used in this protocol expressed GFP and were dissected based on fluorescence. Although not optimal, the dissection of tumors without a reporter can be conducted by location, appearance, and texture. Upon examining the area of the brain where the tumor cells were implanted, features such as changes in color and texture vary between healthy brain and tumors. These features can be used to dissect the murine brain tumors lacking reporters.
c. Dissociate the tumor by applying mechanical pressure with the end of a syringe plunger to push the tumor through the strainer. d. Rinse the strainer four times using 10 mL of media, making sure to keep the tube on ice. 40. Isolate the immune cells.
Note: Refer to ''materials and equipment'' section for instructions on how to prepare the SIP and media solutions.
b. Centrifuge the tumor cells at 300 3 g at 4 C for 5 min. c. Discard the supernatant and resuspend the pellet in 1 mL of media. d. Add the 1 mL cell suspension to 6 mL of media in a 15 mL conical tube labeled ''Tumor with Percoll''. e. Add 3 mL of the SIP solution and mix well. f. Slowly insert a 1 mL serological pipet filled with the 70% Percoll solution along the side of the tube until it reaches the bottom. g. Slowly layer 1 mL of 70% Percoll at the bottom of the tube.
CRITICAL: Exercise caution to very slowly add the 70% Percoll, pipetting too quickly will disturb the gradient and mix the layers.
h. Centrifuge the tube with the gradient at 800 3 g for 20 min at 25 C.
CRITICAL: This centrifugation step must be completed with the brakes off to prevent disrupting the gradient.
i. During centrifugation from the previous step, add 10 mL of media to a 15 mL conical tube labeled ''Tumor after Percoll''. j. Remove the tube from the centrifuge and use a transfer pipette to discard the top layers (containing debris, tumor cells, SIP-media solution) until it approaches the central whitecloudy immune cell layer located between the pink and clear layers (see Figure 3).
CRITICAL: Be careful not to disrupt the gradient when moving the tube.
k. Use another transfer pipette to remove the immune cell layer completely and resuspend it in the labeled ''Tumor after Percoll'' tube. l. Centrifuge the tube at 300 3 g for 5 min at 4 C. m. Discard the supernatant and resuspend in 1 mL of freezing media. n. Transfer to a labeled cryovial.

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o. Immediately place the cryovial in a -20 C freezer for one hour before transferring to a À80 C freezer for 12-18 h.
Note: It is recommended to stain and fix samples for mass cytometry on the same day rather than freezing, if possible.

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Note: The antibody dilutions vary based on the several factors such as the efficiency of the conjugation. Thus, it is optimal to always do the antibody titration when using a new batch of antibodies or using it on a different tissue type (See ''titration of antibodies and assessing signal spillover'' in the ''before you begin'' section).
Step 35 of ''Before you Begin'' and Figure 2 outline choosing the best dilution used for the rest of the protocol. Here, frozen samples are used to generate a single-cell suspension for staining. If the cells were not previously frozen, steps 3-5 should be skipped.

MATERIALS AND EQUIPMENT
1. Warm media and serum-free media to 37 C using a water bath.
Note: The media recipes are in the ''materials and equipment'' section.

Prepare a beaker with bleach for liquid waste.
Note: The amount of bleach should be enough so that the end volume has at least 10% bleach.
3. Label 15 mL conical tubes for the samples, which will be added. 4. Add 9 mL of media into the labeled 15 mL conical tubes. 5. Thaw the samples and quench the freezing media. a. Place the cryovials into the 37 C water bath for about a min or until about 80% thawed. b. Transfer the thawed cell suspensions into the labels 15 mL conical tubes.

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CRITICAL: Once thawed, frozen cell must be transfer quickly to media to minimize toxic exposure to DMSO.
6. Centrifuge the cell suspensions for 5 min at 300 3 g. 7. Discard the supernatant and resuspend in 1 mL of medium. 8. Count the cells using a hemacytometer. 9. Centrifuge for 5 min at 300 3 g and discard supernatant. 10. Resuspend the cells at 2 3 10 7 cells/mL of warm serum-free media.

Timing: 30 min
Cisplatin is used for identifying dead cells during data analysis. 11. Prepare 10 uM (23)   23. Incubate at 25 C for 10 min. 24. To prepare the surface antibody master mix, add the surface antibodies shown in Table 1 (in this case, all antibodies except anti-CD206 and anti-Gr-1) to Maxparâ Cell Staining Buffer to make 50 mL of antibody solution per sample.
Note: The volume of each antibody used for the master mix is based on the concentration determined by the titration (refer to the ''titration of antibodies and assessing signal spillover'' section).
Note: CD206 is stained during the intracellular staining process. Anti-Gr-1 recognizes both Ly6G and Ly6C at different binding sites than either clone of the Ly6G and Ly6C antibodies used in this panel. However, to avoid any potential steric hindrance, it is recommended to do sequential staining for anti-Ly6G and anti-Ly6C, followed by anti-Gr-1.

Fixing and intracellular antibody staining
Timing: 2 h The cells are fixed and permeabilized to allow for entry of the intracellular antigens staining antibodies, which then bind to and label the targeted intracellular antigens. 31. Gently vortex to resuspend the cell pellet in 1 mL of 13 Maxparâ Fix I Buffer. 32. Incubate for 30 min at 25 C to fix the cells. 33. During incubation, make 100 mL intercellular antibody master mix per sample by adding the intracellular antibodies to Maxparâ Perm-S Buffer. In this case, anti-CD206 is the only antibody used for intracellular labeling.
Note: The volume of each antibody used for the master mix is based on the concentration determined by the titration (refer to the ''titration of antibodies and assessing signal spillover'' section).
Note: If the intracellular target is a transcription factor or an intranuclear protein, it is recommended to perform the permeabilization using the Maxparâ Nuclear Antigen Staining Buffer Set (Fluidigm, Cat# 201063).
34. Wash the cells. a. Add 2 mL of Maxparâ Perm-S Buffer. b. Centrifuge at 800 3 g for 5 min. c. Discard supernatant. 35. Repeat the wash for a total of 2 washes. 36. Gently resuspend the pellet in 100 mL of intracellular antibody stain. 37. Incubate for 30 min at 25 C.

OPEN ACCESS
38. Wash the cells two more times as done in step 34.

Fresh fix and DNA intercalator staining
Timing: 45 min, left overnight (12-18 h) In this step, the cells are stained with DNA intercalator, which allows for downstream identification of cell singlets. As DNA intercalator binds to nucleic acids, and cell-cell doublets have more DNA, doublets have a stronger DNA intercalator signal than singlets. Note: This solution can be prepared during the centrifugation in step 41 to save time.
44. Add 1 mL of the 13 intercalation solution and gently vortex to mix. 45. Incubate overnight (12-18 h) at 4 C to label the nucleated cells.
Pause point: The cells can be left up to 2 days at 4 C until acquisition.

Sample acquisition on CyTOFâ instrument
Timing: 1 day (3-4 h per sample) It is important to run the samples along with Fluidigm's EQä Four Element Calibration Beads, which allow for changes during sample acquisition to be monitored. The steps for sample acquisition and monitoring are as follow: 46. Wash the cells. a. Add 3 mL of Maxparâ Cell Staining Buffer. b. Centrifuge at 800 3 g for 5 min. c. Discard supernatant. 47. Resuspend the cells in 1 mL of Milli-Q water and count the cells. 48. Add an additional 3 mL to wash the cells and centrifuge at 800 3 g for 5 min. 49. Resuspend the pelleted cells with Milli-Q water containing 0.13 EQä Four Element Calibration Beads (diluted from 13 stock bottle) according to cell count to achieve 8-10 3 10 5 cells/mL. 50. Filter cells with a 40 mm cell strainer prior to introduction into the Heliosä, a CyTOFâ System. Note: Changes in sample acquisition can happen due to variations in instrument performance. Because of these variations in instrument performance, quality control of the instrument before and during sample acquisition is essential.
Note: This protocol focuses on the preparation and staining of cell suspensions along with the analysis of mass cytometry data. The process of acquiring the sample data is beyond the scope of this protocol. As such, samples should be submitted to a mass cytometry core specifically trained to run the samples for data acquisition.

Normalization
Timing: 1 h Data normalization is crucial to minimize technical variation. This process can be performed using the Fluidigm CyTOFâ software, which uses a standard ''Bead passport'' with standardized target values for the calibration beads. Using this tool, a bead's signal is detected and recorded throughout the sample acquisition, and the median intensity is calculated within a user-defined time interval. By assuming a linear rate of signal decay, the rate constant of signal decay in each interval is calculated against the global mean of signal intensity. Every event within the specified interval is multiplied by the rate constant. 51. Using the CyTOFâ software, select the raw FCS file, choose the ''Process'' tab, and select FCS processing. 52. Select the corresponding bead passport to the EQä Four Element Calibration Beads lot number used in data acquisition. 53. Select the time interval that the normalization factor will be applied to.
Note: By default, the software will use a 100 s interval.
Note: Another method of normalization was developed by the Nolan lab to mitigate the impact of signal drift on the data.

Concatenation (optional)
Timing: 1 h Sample acquisition may need to be run in various rounds because of the volume of the sample or a change in system performance during acquisition, such as a clog or unexpected shutdown. This results in multiple FCS files for a single sample which can then be concatenated into a single file. 54. In the CyTOFâ Software, select the FCS processing tab. Then select the raw FCS files to concatenate. 55. Click on the ''concatenate'' tab. The concatenated FCS file will be generated and ready for export.
CRITICAL: Make sure that the original data box is checked.
CRITICAL: Route the name and the directory of the new concatenated file. The normalization process will take place automatically once you concatenate.
Note: Instead of the CyTOFâ software, concatenation may be performed using Cytobank. However, data must be normalized before using this tool.

EXPECTED OUTCOMES
Due to the large number of antibodies used to run the mass cytometry panel, data analysis can be complex. This is particularly true in situations that require annotation of subpopulations that express a previously unidentified marker. However, mass cytometry analysis is facilitated by a number of publicly available analysis tools that rely mainly on dimensionality reduction. Several publications examine these tools in higher detail, which are not within the scope of this protocol (Kimball et al., 2018;Liu et al., 2019;Matos et al., 2017;Weber and Robinson, 2016). Here, we will discuss some commonly used advanced analytical tools available in Cytobank in the context of the questions they were designed to answer. We will also discuss the advantages and disadvantages of each analysis method and the ideal situation in which to run each one of them, which depends heavily on the biological question being asked.

QUANTIFICATION AND STATISTICAL ANALYSIS
A standard mass cytometry analysis workflow consists of the following steps: (1) Identification of different cell subsets using manual or automated gating detection, (2) Applying a clustering algorithm to detect the abundance of a specific cell subset and/or differential biomarker expression in cluster subsets across different conditions, and (3) visualization of cell clusters using dimensionality reduction.

Data visualization and gating
The first step of mass cytometry data analysis is performing data cleanup. This consists of the removal of unwanted events, dead cells, debris, normalization beads, true aggregates, and coincident ion clouds from raw data. The most common approach is to use the Gaussian distributionbased models available on Fluidigm Heliosä and CyTOFâ instruments. The four Gaussian parameters, residual, offset, center, and width, can be used to describe the shape of the distribution of pushes in a pulse, where a push is each instance of ions being sent to the detector and an individual pulse is composed of pushes integrated together to form an event (see Figure 4) (Lee and Rahman, 2019). These parameters are valuable for detecting well-formed pulses, eliminating coincident positive ion clouds, and to further exclude cell doublets. An example of using Gaussian-based parameter for single cell gating is illustrated in Figure 4.
To perform data cleanup: 1. Gate out the EQä Four Element Calibration Beads by plotting 140Ce against Time.
Note: 140Ce is tagged to the EQä Four Element Calibration Beads. 195Pt is from the Cell-IDä Cisplatin. 191Ir and 193Ir are from the Cell-IDä Intercalator.
2. Gate on the four Gaussian parameters sequentially (i.e., residual, center, offset, width) to eliminate unwanted non-Gaussian pulses as shown in Figure 4. 3. Select the ideal metal ion content and ion cloud size by plotting Event Length vs Time. 4. Gate on the live cells by plotting 195Pt vs time. 5. Remove doublets by applying two DNA gates (i.e.,191Ir,and 193Ir) vs Time. In both plots, the doublets will be the cloud with higher DNA content.

Automated gating
After data cleanup, the next step is to identify the different cell populations. This can be done using supervised or unsupervised gating to identify populations of interest. Below we will compare the advantages and the disadvantages of each method.

Unsupervised gating
This approach is done via clustering methods which place cell populations with similar phenotypes together in a low dimensional space. This allows the discovery of unknown cell populations from high dimension data analysis in a large dataset by comparing two groups (such as, control vs treated). A variety of advanced approaches are available to perform such analysis. For example, FlowSOM, SPADE, and Phonograph are tools used to perform high dimensional data analysis based on automated clustering. A variety of user-defined parameters, such as k-mean value, can determine the resolution and subculturing of immune cell populations. It is important to set these values to represent the expected subpopulations and avoid over clustering.

Supervised gating
This approach is useful when the experimental expectations and the cell type compositions in the samples are known. It is useful to discover and validate novel biomarkers by comparing two populations of interest in distinct groups through high-dimensional analysis. FlowDensity and OpenCyto are useful tools to perform automated gating using user-defined gates. The main advantage of using these tools is that the threshold for identifying the positive population is adjusted in a data-dependent manner for each sample rather than manually gating each sample, which can be subjective.

Cell subset detection (clustering)
Many clustering algorithms for mass cytometry have been published in recent years. However, the differences in their accuracy and sensitivity when identifying different cell populations is still a point of debate. An ideal algorithm for analyzing mass cytometry data should not only detect all major immune cell populations but also detects rare cell populations. Below, we discuss some commonly used clustering algorithms in Cytobank.
t-SNE: Clustering on dimensionality reduction channels (e.g., viSNE) is a more practical approach for samples consisting of heterogeneous cell subsets (high dimensional data). These include samples ll OPEN ACCESS containing cells with different and/or variable biomarker expression. In these situations, it is important to do t-SNE dimensionality reduction before applying any clustering algorithm such as SPADE or FlowSOM. However, because each dataset is different, it is difficult to determine when this approach is needed. Therefore, data must be explored in depth to determine the expression pattern and the heterogeneity of the sample. An example of using the t-SNE clustering approach to identify different immune cells infiltrating a GBM tumor microenvironment is shown in Figure 5.
Spanning-tree progression analysis for density-normalized events (SPADE): SPADE is one of the most widely used algorithms for immune cell mass cytometry data analysis. The main advantages of SPADE are that it overcomes computational challenges while lowering the risk of missing rare populations by performing density-dependent downsampling before hierarchical clustering. Density-dependent downsampling is an algorithm that extracts events to normalize the cell density. This allows for smaller cell populations not be overshadowed by larger cell populations. Utilizing hierarchical clustering enables SPADE to capture the continuity of phenotypes in the hematopoietic system.
FlowSOM: FlowSOM reveals how all markers behave on all cells by utilizing Self-Organizing Maps (SOMs) to shorten analysis time and improve clustering quality. The main advantage of this algorithm is that it is quick and eliminates the need for downsampling. In addition, it avoids the need to define the number of clusters in the output, which is a major drawback of SPADE which can introduce bias to the analysis.

LIMITATIONS
Be wary of environmental metal contamination. Reagents from other companies and certain dish cleaning detergents can add unwanted metal contamination. Additionally, this technique should be conducted in an area without traces of metals in the air, such as those generated when using flints and Bunsen burners.
This protocol has the preparation of the antibody mixes on the day of the main staining experiment.
To shorten the experiment time, the antibody mixes can be made the day prior and stored at 4 C in the dark.
Tissues can be harvested on the day of the main staining experiment to reduce loss of cell viability at the cost of a lengthier main experiment day.

TROUBLESHOOTING
Problem 1 When conjugating the purified antibodies to lanthanide metal labels, it is difficult to manage the timing of the different incubation periods due to only having one centrifuge, especially when the antibody reduction requires an exactly 30 min incubation (see step 2 in the ''conjugating the antibodies to metal isotopes'' section).

Potential solution
While the protocol may recommend that the polymer and antibody be processed at the same time, the polymer washes can be delayed until after the antibody reduction and purification are complete. The antibody reduction must be completed with the incubation timings given to prevent undesirable conjugations. On the other hand, the polymer wash can be delayed without consequence.

Problem 2
Since samples will be resuspended in water just prior to acquisition, cells are prone to degradation and debris formation (see step 49).

Potential solution
Proper fixation will protect cells from the hypotonic environment of the water before data acquisition.

Problem 3
Detector voltage may vary during sample acquisition, which may create high variability between samples (see step 50).

Potential solution
As part of the quality control for each sample, it is important to check the intensity of the beads in each sample. The best way to do that is by checking the median signal intensity of Eu153 on the beads and comparing it to the reference beads' intensity value. If the signal drops more than 25% in any sample, stop the acquisition, and recalibrate the detector voltage before running the sample again. Also, the coefficient of variance (CV) of the beads is reflective of the amount of error during ll OPEN ACCESS data acquisition. High CVs are indicative of a reduced ability to accurately detect minor changes in protein expression levels between cells.

Problem 4
During sample acquisition, oxidation of metal conjugated antibodies may occur, resulting in a 16 dalton (Da) increase in the mass channel of that isotope (for example Nd 146 oxides will be recorded as Dy162). This can be a source of cross reactivity between the metal isotopes and should be considered during the panel design (see step 50).

Potential solution
EQä Four Element Calibration Beads can be used to monitor alteration due to oxide formation during sample acquisition by calculating the ratio between the dual counts of Ce140 and Gd156. Also, Fluidigm recommends tuning the instrument using CyTOFâ Tuning Solution (Fluidigm, SKU, 201072) to a ratio threshold of 3%. Due to the trade-off nature between oxidation and signal intensity, increasing signal intensity during tuning typically indicates a higher oxidation ratio.

Problem 5
Cell signal intensity may decrease during acquisition because of instrument performance or sample degradation.

Potential solution
While instrument performance can be tracked using EQä Four Element Calibration Beads, cell-specific degradation cannot. First, identify the Ce140+ DNAÀ EQä Four Element Calibration Beads population and the Ce140ÀDNA+ cell population. Next, plot time vs. a measured parameter (e.g., Eu153 for beads or CD45 for cells) and calculate the slope for each. A slight negative slope is generally expected, reflecting gradual loss of instrument sensitivity over time. A steeper slope for both cells and beads indicates a more rapid loss of instrument sensitivity over the period of acquisition, while a steeper slope for measured cell parameters relative to the measured bead parameters can indicate sample degradation. This sample degradation is indicative of poor sample fixation.

RESOURCE AVAILABILITY
Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Maria G. Castro, mariacas@med.umich.edu.

Materials availability
This study involved tumor tissue from SB glioma mouse models. The protocol for generation of this model is as described in Garcia-Fabiani et al. (2020).

Data and code availability
No datasets or code was generated or analyzed during this study.


A catalogue of the Lonchaeidae (Diptera: Tephritoidea) and Teratomyzidae (Diptera: Opomyzoidea) of Chile

. A catalogue of the Lonchaeidae and Teratomyzidae of Chile is presented. This paper comprises two genera with five species of Lonchaeidae and two genera with two species of Teratomyzidae known from Chile, including information about the name of the taxon, author, year of publication, page number, type species, type depository, type locality, and references. The geographical distributions of each species were determined by examination of bibliographic data and of label data on specimens in collections.

Species of Lonchaeidae occur worldwide and are found in a wide range of habitats, from the Arctic Circle to the Equator and in all zoogeographical regions (MacGowan & Friedberg, 2008). Most lonchaeids are scavengers as larvae, breeding mainly in damaged plant tissues or decaying vegetation, but a few species are primary invaders of living plants (Norrbom & Korytkowski, 2010).
Members of Lonchaeidae can be recognised by the following characters: moderately small (body length 3.0-6.0 mm), relatively setose, shiny black to metallic blue or green, with wings usually hyaline. Body stout, abdomen broad and flat. Face broad, usually without strong carina. Eye large, pilose or bare. Without vibrissa but one or more sub vibrissal vibrissae-like setae sometimes present. Wing with anal lobe and alula well developed, halter dark brown (Norrbom & Korytkowski, 2010;MacGowan & Rotheray, 2021).
Currently Teratomyzidae comprises seven genera and 24 species, distributed in the Australasian/ Oceanic, Oriental, and southern Neotropical regions. The systematic position of Teratomyzidae has been controversial. Courtney et al. (2017) and Pape et al. (2011) placed Teratomyzidae in the Opomyzoidea group, whereas Malloch (1933) placed the genera Teratomyza of Teratomyzidae in Anthomyzidae. D.K. McAlpine (1998) countered that teratomyzids further differ from the Anthomyzidae in the reduction of the prosternum and from the Opomyzidae in the presence of the vibrissae. More recently, Teratomyzidae has been placed in the Sphaeroceroidea, closely to Heleomyzidae and Paraleucopidae (Bayless et al., 2021;Bayless, 2022). Little known about the biology of Teratomyzidae. According to McAlpine & Keizer (1994), most teratomyzids were obtained by sweeping fronds of terrestrial ferns, usually in or near the margins of forests. D.K. McAlpine (1998) provided a diagnosis to Teratomyzidae: CuA₂+A₁ terminating well beyond the level of crossvein dm-cu, no elongate costal bristle overlapping the costal break, alula broad and produced distad of the alular incision, several fronto-orbital setae present, parafacial suture distinct, a large supra-alar seta present, no abdominal sternite 6 in the male, and a distinctively modified postabdomen in the female.
The goal of this paper is to provide a catalogue of the Lonchaeidae of Chile and an updated cata-logue to the Teratomyzidae of Chile with references and additional information for each species, and genus of both families.

MATERIAL AND METHODS
For each species, the Chilean distribution is given by region and province (from north to south) and localities (alphabetically) when such data are available from the literature and from specimens in collections.

RESULTS
Two genera with five species of Lonchaeidae and two genera with two species of Teratomyzidae have been recorded from Chile (Table 1).